提交 8c95057b 编写于 作者: C chenguowei01

update README.md

上级 4db3b825
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## 预训练模型
HumanSeg开放了在大规模人像数据上训练的三个预训练模型,满足多种使用场景的需求
| 模型类型 | Checkpoint | Inference Model | Quant Inference Model | 备注 |
| --- | --- | --- | ---| --- |
| HumanSeg-server | [humanseg_server_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_ckpt.zip) | [humanseg_server_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_inference.zip) | -- | 高精度模型,适用于服务端GPU且背景复杂的人像场景, 模型结构为Deeplabv3+/Xcetion65 |
| HumanSeg-mobile | [humanseg_mobile_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_ckpt.zip) | [humanseg_mobile_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_inference.zip) | [humanseg_mobile_quant](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_quant.zip) | 轻量级模型, 适用于移动端或服务端CPU的前置摄像头场景,模型结构为HRNet_w18_samll_v1 |
| HumanSeg-lite | [humanseg_lite_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_ckpt.zip) | [humanseg_lite_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_inference.zip) | [humanseg_lite_quant](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_quant.zip) | 超轻量级模型, 适用于手机自拍人像,且有移动端实时分割场景, 模型结构为优化的ShuffleNetV2 |
模型计算耗时(小米,cpu:骁龙855, 内存:6GB, 图片大小:192*192)
| 模型 | humanseg_mobile_inference | humanseg_mobile_quant | humanseg_lite_inference | humanseg_lite_quant |
| --- | --- | --- | --- | --- |
| HumanSeg-server | [humanseg_server_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_ckpt.zip) | [humanseg_server_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_server_inference.zip) | -- | 高精度模型,适用于服务端GPU且背景复杂的人像场景 |
| HumanSeg-mobile | [humanseg_mobile_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_ckpt.zip) | [humanseg_mobile_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_inference.zip) | [humanseg_mobile_quant](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_mobile_quant.zip) | 轻量级模型, 适用于移动端或服务端CPU的前置摄像头场景 |
| HumanSeg-lite | [humanseg_lite_ckpt](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_ckpt.zip) | [humanseg_lite_inference](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_inference.zip) | [humanseg_lite_quant](https://paddleseg.bj.bcebos.com/humanseg/models/humanseg_lite_quant.zip) | 超轻量级模型, 适用于手机自拍人像,且有移动端实时分割场景 |
| 耗时(ms) | 42.25 | 24.93 | 17.26 | 11.89 |
**NOTE:**
其中Checkpoint为模型权重,用于Fine-tuning场景。
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```
## 快速体验视频流人像分割
结合DIS(Dense Inverse Search-basedmethod)光流算法预测结果与分割结果,改善视频流人像分割
```bash
# 通过电脑摄像头进行实时分割处理
python video_infer.py --model_dir pretrained_weights/humanseg_lite_inference
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python video_infer.py --model_dir pretrained_weights/humanseg_lite_inference --video_path data/video_test.mp4
```
视频分割结果如下:
<img src="https://paddleseg.bj.bcebos.com/humanseg/data/video_test.gif" width="20%" height="20%"><img src="https://paddleseg.bj.bcebos.com/humanseg/data/result.gif" width="20%" height="20%">
**NOTE**:
视频分割处理时间需要几分钟,请耐心等待。
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